Maximilian Lombardo

Hundreds of millions of single cells have been analyzed using high-throughput transcriptomic methods. The cumulative knowledge within these datasets provides an exciting opportunity for unlocking insights into health and disease at the level of single cells. Meta-analyses that span diverse datasets building on recent advances in large language models and other machine-learning approaches pose exciting new directions to model and extract insight from single-cell data. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, the sheer number of datasets, data models and accessibility remains a challenge. Here, we present CZ CELLxGENE Discover (cellxgene.cziscience.com), a data platform that provides curated and interoperable single-cell data. Available via a free-to-use online data portal, CZ CELLxGENE hosts a growing corpus of community-contributed data of over 93 million unique cells. Curated, standardized and associated with consistent cell-level metadata, this collection of single-cell transcriptomic data is the largest of its kind and growing rapidly via community contributions. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to explore individual datasets, perform cross-corpus analysis, and run meta-analyses of tens of millions of cells across studies and tissues at the resolution of single cells.

Abstract Quickly and flexibly exploring high-dimensional datasets, such as scRNAseq data, is underserved but critical for hypothesis generation, dataset annotation, publication, sharing, and community reuse. cellxgene is a highly generalizable, web-based interface for exploring high dimensional datasets along categorical, continuous and spatial dimensions, as well as feature annotation. cellxgene is differentiated by its ability to performantly handle millions of observations, and bridges a critical gap by enabling computational and experimental biologists to iteratively ask questions of private and public datasets. In doing so, cellxgene increases the utility and reusability of datasets across the single-cell ecosystem. The codebase can be accessed at https://github.com/chanzuckerberg/cellxgene . For questions and inquiries, please contact cellxgene@chanzuckerberg.com .

Abstract Hundreds of millions of single cells have been analyzed to date using high throughput transcriptomic methods, thanks to technological advances driving the increasingly rapid generation of single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent advances in large language models and other machine learning approaches. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, a major challenge remains the sheer number of datasets and inconsistent format, data models and accessibility. Many datasets are available via unique portals platforms that often lack interoperability. Here, we present CZ CellxGene Discover ( cellxgene.cziscience.com ), a data platform that provides curated and interoperable data. This single-cell data resource, available via a free-to-use online data portal, hosts a growing corpus of community contributed data that spans more than 50 million unique cells. Curated, standardized, and associated with consistent cell-level metadata, this collection of interoperable single-cell transcriptomic data is the largest of its kind. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to rapidly explore individual datasets and perform cross-corpus analysis. This functionality is enabling meta-analyses of tens of millions of cells across studies and tissues and providing global views of human cells at the resolution of single cells.

PURPOSE: Pan-class histone deacetylase (HDAC) inhibitors are effective treatments for select lymphomas. Isoform-selective HDAC inhibitors are emerging as potentially more targeted agents. HDAC6 is a class IIb deacetylase that facilitates misfolded protein transport to the aggresome for degradation. We investigated the mechanism and therapeutic impact of the selective HDAC6 inhibitor ACY-1215 alone and in combination with bortezomib in preclinical models of lymphoma. EXPERIMENTAL DESIGN: Concentration-effect relationships were defined for ACY-1215 across 16 lymphoma cell lines and for synergy with bortezomib. Mechanism was interrogated by immunoblot and flow cytometry. An in vivo xenograft model of DLBCL was used to confirm in vitro findings. A collection of primary lymphoma samples were surveyed for markers of the unfolded protein response (UPR). RESULTS: Concentration-effect relationships defined maximal cytotoxicity at 48 hours with IC50 values ranging from 0.9 to 4.7 μmol/L. Strong synergy was observed in combination with bortezomib. Treatment with ACY-1215 led to inhibition of the aggresome evidenced by acetylated α-tubulin and accumulated polyubiquitinated proteins and upregulation of the UPR. All pharmacodynamic effects were enhanced with the addition of bortezomib. Findings were validated in vivo where mice treated with the combination demonstrated significant tumor growth delay and prolonged overall survival. Evaluation of a collection of primary lymphoma samples for markers of the UPR revealed increased HDAC6, GRP78, and XBP-1 expression as compared with reactive lymphoid tissue. CONCLUSIONS: These data are the first results to demonstrate that dual targeting of protein degradation pathways represents an innovative and rational approach for the treatment of lymphoma.

Abstract Purpose: Pan-class I/II histone deacetylase (HDAC) inhibitors are effective treatments for select lymphomas. Isoform-selective HDAC inhibitors are emerging as potentially more targeted agents. ACY-1215 (ricolinostat) is a first-in-class selective HDAC6 inhibitor. To better understand the discrete function of HDAC6 and its role in lymphoma, we developed a lymphoma cell line resistant to ACY-1215. Experimental Design: The diffuse large B-cell lymphoma cell line OCI-Ly10 was exposed to increasing concentrations of ACY-1215 over an extended period of time, leading to the development of a resistant cell line. Gene expression profiling (GEP) was performed to investigate differentially expressed genes. Combination studies of ACY-1215 and ibrutinib were performed in cell lines, primary human lymphoma tissue, and a xenograft mouse model. Results: Systematic incremental increases in drug exposure led to the development of distinct resistant cell lines with IC50 values 10- to 20-fold greater than that for parental lines. GEP revealed upregulation of MAPK10, HELIOS, HDAC9, and FYN, as well as downregulation of SH3BP5 and LCK. Gene-set enrichment analysis (GSEA) revealed modulation of the BTK pathway. Ibrutinib was found to be synergistic with ACY-1215 in cell lines as well as in 3 primary patient samples of lymphoma. In vivo confirmation of antitumor synergy was demonstrated with a xenograft of DLBCL. Conclusions: The development of this ACY-1215–resistant cell line has provided valuable insights into the mechanistic role of HDAC6 in lymphoma and offered a novel method to identify rational synergistic drug combinations. Translation of these findings to the clinic is underway. Clin Cancer Res; 23(12); 3084–96. ©2016 AACR.